Las enfermedades cardiovasculares son la principal causa de muerte en el mundo, y se calcula que cobran 17,9 millones de vidas al año (OMS). La enfermedad de las arterias coronarias es el tipo más común de enfermedad cardíaca y se produce debido a las obstrucciones (placa) desarrolladas en el interior de las arterias coronarias (vasos sanguíneos que alimentan los músculos del corazón). Los cardiólogos utilizan diversas técnicas de imagen y mediciones invasivas de la presión arterial para examinar y controlar la gravedad de dichas obstrucciones.
Los factores de riesgo conductuales más importantes de estas enfermedades son una dieta poco saludable, la inactividad física, el consumo de tabaco y el uso nocivo del alcohol. Los efectos de los factores de riesgo pueden manifestarse en las personas en forma de aumento de la presión arterial, aumento de la glucosa en sangre, aumento de los lípidos en sangre y sobrepeso y obesidad.
Identificar a las personas con mayor riesgo de sufrir enfermedades cardiovasculares y garantizar que reciban el tratamiento adecuado puede evitar muertes prematuras. Con este objetivo en mente, se quiere utilizar las técnicas de machine learning para construir un modelo que permita predecir qué pacientes pueden estar en riesgo de padecer este tipo de cardiopatía.
Referencias.
OMS (s.f.). “Cardiovascular diseases”. https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1
Fuente de Datos: https://www.kaggle.com/agsam23/coronary-artery-disease/version/3
En primer lugar, se importan todas las librerías requeridas para realizar la lectura, preprocesamiento y construcción del modelo incluyendo las librerías re
# Data management Libraries
import pandas as pd
import numpy as np
import sys
from pandas_profiling import ProfileReport
# Data Modeling Libraries
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report, plot_confusion_matrix
from sklearn.compose import ColumnTransformer
# Data visualization Libraries
import matplotlib.pyplot as plt
import seaborn as sns
#Save the model
from joblib import dump, load
La fuente de datos otorgada cuenta con tres archivos: el primero con los datos crudos, el segundo con los datos clasificados y el tercero con estos últimos particionados en cinco subconjuntos de forma estratificada. Considerando que en datasets reducidos, como es el caso en cuestión, es natural tomar el 20% del conjunto total para hacer pruebas sobre el modelo seleccionado (test set), se optó por hacer uso del último archivo con el propósito de aprovechar las particiones estratificadas y de tener así el conjunto de prueba segmentado desde un principio.
data_df = pd.read_csv('st_fold_data.csv')
data_df.head()
| Unnamed: 0 | age | sex | cp | trestbps | chol | fbs | restecg | thalach | exang | oldpeak | slope | ca | thal | label | kfold | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 63.0 | 1.0 | 1.0 | 145.0 | 233.0 | 1.0 | 2.0 | 150.0 | 0.0 | 2.3 | 3.0 | 0.0 | 6.0 | 0 | 0.0 |
| 1 | 1 | 67.0 | 1.0 | 4.0 | 160.0 | 286.0 | 0.0 | 2.0 | 108.0 | 1.0 | 1.5 | 2.0 | 3.0 | 3.0 | 2 | 0.0 |
| 2 | 2 | 67.0 | 1.0 | 4.0 | 120.0 | 229.0 | 0.0 | 2.0 | 129.0 | 1.0 | 2.6 | 2.0 | 2.0 | 7.0 | 1 | 0.0 |
| 3 | 3 | 37.0 | 1.0 | 3.0 | 130.0 | 250.0 | 0.0 | 0.0 | 187.0 | 0.0 | 3.5 | 3.0 | 0.0 | 3.0 | 0 | 0.0 |
| 4 | 4 | 41.0 | 0.0 | 2.0 | 130.0 | 204.0 | 0.0 | 2.0 | 172.0 | 0.0 | 1.4 | 1.0 | 0.0 | 3.0 | 0 | 0.0 |
Una vez importados los datos, es necesario conocer sus campos de manera general por lo que se realiza un perfilamiento de los mismos a través de la librería pandas_profiling que retorna de forma renderizada un reporte simple como se muestra a continuación.
ProfileReport(data_df)
Con base en el perfilamiento, fue posible evidenciar que, aunque no hay valores faltantes en ninguno de los campos, existen en total 6 registros donde uno de los atributos 'ca' o 'thal' toman el valor de '?'. Dado que esta cantidad de registros es insignificante en comparación con el tamaño del dataset inicial (303 datos), se decidió eliminar dichos registros. De igual manera, puesto que se busca identificar la presencia o ausencia de la enfermedad de las arterias coronarias independientemente de la etapa en la que se encuentra,
data_df = data_df[data_df['ca'] != '?']
data_df = data_df[data_df['thal'] != '?']
data_df['label'] = (data_df['label'] > 0).astype('int32')
data_df.head()
| Unnamed: 0 | age | sex | cp | trestbps | chol | fbs | restecg | thalach | exang | oldpeak | slope | ca | thal | label | kfold | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 63.0 | 1.0 | 1.0 | 145.0 | 233.0 | 1.0 | 2.0 | 150.0 | 0.0 | 2.3 | 3.0 | 0.0 | 6.0 | 0 | 0.0 |
| 1 | 1 | 67.0 | 1.0 | 4.0 | 160.0 | 286.0 | 0.0 | 2.0 | 108.0 | 1.0 | 1.5 | 2.0 | 3.0 | 3.0 | 1 | 0.0 |
| 2 | 2 | 67.0 | 1.0 | 4.0 | 120.0 | 229.0 | 0.0 | 2.0 | 129.0 | 1.0 | 2.6 | 2.0 | 2.0 | 7.0 | 1 | 0.0 |
| 3 | 3 | 37.0 | 1.0 | 3.0 | 130.0 | 250.0 | 0.0 | 0.0 | 187.0 | 0.0 | 3.5 | 3.0 | 0.0 | 3.0 | 0 | 0.0 |
| 4 | 4 | 41.0 | 0.0 | 2.0 | 130.0 | 204.0 | 0.0 | 2.0 | 172.0 | 0.0 | 1.4 | 1.0 | 0.0 | 3.0 | 0 | 0.0 |
data_training = data_df[data_df['kfold']<4]
data_test = data_df[data_df['kfold']==4]
training_X = data_training.drop(['label'],axis=1)
training_Y = data_training['label']
ct = ColumnTransformer(transformers=[('drop', 'drop', ['Unnamed: 0', 'kfold']),
('one_hot', OneHotEncoder(handle_unknown='ignore'), ['cp','restecg','slope','ca','thal']),
('normalize', MinMaxScaler(), ['age','trestbps','chol','thalach','oldpeak'])],
remainder='passthrough')
model = Pipeline([
('preprocessing', ct),
('algoritmo',LogisticRegression(solver='liblinear'))
])
model.fit(training_X,training_Y)
plot_confusion_matrix(model,training_X,training_Y,values_format='d')
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1325ef48188>
param_grid_logr = dict(algoritmo__penalty=['l2', 'elasticnet','none'],
algoritmo__C=[1e-5, 1e-4, 1e-3, 1e-2, 0.1, 1],
algoritmo__solver=['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
algoritmo__max_iter=[10,20,50,100],
preprocessing__normalize=[MinMaxScaler(), StandardScaler()])
grid_search_logr = GridSearchCV(model,param_grid=param_grid_logr,cv=5,verbose=3,scoring='accuracy')
grid_search_logr.fit(training_X,training_Y.ravel())
Fitting 5 folds for each of 720 candidates, totalling 3600 fits [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio "
[CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio "
[CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.958 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.958 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.894 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.958 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.958 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
[CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters "Setting penalty='none' will ignore the C and l1_ratio " x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations. "number of iterations.", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations. "number of iterations.", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations. "number of iterations.", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations. "number of iterations.", ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\utils\optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
"number of iterations.", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.1s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=10, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=20, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=50, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=l2, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 451, in _check_solver
" got solver={}.".format(solver))
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 444, in _check_solver
"got %s penalty." % (solver, penalty))
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1315, in fit
" got (l1_ratio=%r)" % self.l1_ratio)
ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)
FitFailedWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=elasticnet, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=newton-cg, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
[CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=lbfgs, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=liblinear, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 1306, in fit
solver = _check_solver(self.solver, self.penalty, self.dual)
File "x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py", line 455, in _check_solver
"penalty='none' is not supported for the liblinear solver"
ValueError: penalty='none' is not supported for the liblinear solver
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=sag, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge "the coef_ did not converge", ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=MinMaxScaler();, score=0.872 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__max_iter=100, algoritmo__penalty=none, algoritmo__solver=saga, preprocessing__normalize=StandardScaler();, score=0.872 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_search.py:925: UserWarning: One or more of the test scores are non-finite: [0.54397163 0.54397163 0.54397163 0.54397163 0.83235816 0.78652482
0.54397163 0.54397163 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.84113475 0.85363475 0.84937943
nan nan 0.85363475 0.84521277 0.84503546 0.85762411
0.54397163 0.54397163 0.54397163 0.54397163 0.83235816 0.78652482
0.54397163 0.54397163 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83687943 0.84113475
nan nan 0.84946809 0.84521277 0.8535461 0.8535461
0.54397163 0.54397163 0.54397163 0.54397163 0.83235816 0.78652482
0.54397163 0.54397163 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.84113475 0.84113475 0.84530142 0.84937943
0.54397163 0.54397163 0.54397163 0.54397163 0.83235816 0.78652482
0.54397163 0.54397163 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.83696809 0.83696809 0.84113475 0.84113475
0.54397163 0.54397163 0.54397163 0.54397163 0.84069149 0.78652482
0.54397163 0.54397163 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.84113475 0.85363475 0.84937943
nan nan 0.84521277 0.85345745 0.85771277 0.85345745
0.54397163 0.54397163 0.54397163 0.54397163 0.84069149 0.78652482
0.54397163 0.54397163 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83687943 0.84113475
nan nan 0.84521277 0.84937943 0.85771277 0.8535461
0.54397163 0.54397163 0.54397163 0.54397163 0.84069149 0.78652482
0.54397163 0.54397163 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.84113475 0.84113475 0.84530142 0.84937943
0.54397163 0.54397163 0.54397163 0.54397163 0.84069149 0.78652482
0.54397163 0.54397163 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.83696809 0.83696809 0.84113475 0.84113475
0.54397163 0.62757092 0.54397163 0.62757092 0.83661348 0.79078014
0.54397163 0.62757092 0.54397163 0.62757092 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.84113475 0.85363475 0.84937943
nan nan 0.84095745 0.84929078 0.85345745 0.85762411
0.54397163 0.62757092 0.54397163 0.62757092 0.83661348 0.79078014
0.54397163 0.62757092 0.54397163 0.62757092 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83687943 0.84113475
nan nan 0.84521277 0.84937943 0.85771277 0.8535461
0.54397163 0.62757092 0.54397163 0.62757092 0.83661348 0.79078014
0.54397163 0.62757092 0.54397163 0.62757092 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.84113475 0.84113475 0.84530142 0.85363475
0.54397163 0.62757092 0.54397163 0.62757092 0.83661348 0.79078014
0.54397163 0.62757092 0.54397163 0.62757092 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.83696809 0.83696809 0.84113475 0.84113475
0.83661348 0.79902482 0.83661348 0.79902482 0.85753546 0.80753546
0.83661348 0.79902482 0.83661348 0.79902482 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.84113475 0.85363475 0.84937943
nan nan 0.85762411 0.84937943 0.85345745 0.85762411
0.83661348 0.79902482 0.83661348 0.79902482 0.85753546 0.80753546
0.83661348 0.79902482 0.83661348 0.79902482 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83687943 0.84113475
nan nan 0.84955674 0.84937943 0.85780142 0.85771277
0.83661348 0.79902482 0.83661348 0.79902482 0.85753546 0.80753546
0.83661348 0.79902482 0.83661348 0.79902482 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.84113475 0.84113475 0.84530142 0.84937943
0.83661348 0.79902482 0.83661348 0.79902482 0.85753546 0.80753546
0.83661348 0.79902482 0.83661348 0.79902482 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.83696809 0.83696809 0.84113475 0.84113475
0.85762411 0.8535461 0.85336879 0.8535461 0.84911348 0.84937943
0.85762411 0.8535461 0.85762411 0.84937943 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.84113475 0.85363475 0.84937943
nan nan 0.83661348 0.86196809 0.85771277 0.86187943
0.85762411 0.8535461 0.85762411 0.8535461 0.84911348 0.84937943
0.85762411 0.8535461 0.85762411 0.8535461 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83687943 0.84113475
nan nan 0.84521277 0.84937943 0.85780142 0.86196809
0.85762411 0.8535461 0.85762411 0.8535461 0.84911348 0.84937943
0.85762411 0.8535461 0.85762411 0.8535461 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.84113475 0.83696809 0.84530142 0.84937943
0.85762411 0.8535461 0.85762411 0.8535461 0.84911348 0.84937943
0.85762411 0.8535461 0.85762411 0.8535461 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.83696809 0.83696809 0.84113475 0.84113475
0.84920213 0.85345745 0.85345745 0.85345745 0.84911348 0.85771277
0.84494681 0.85771277 0.84911348 0.8535461 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.84113475 0.85363475 0.84937943
nan nan 0.84095745 0.85771277 0.85345745 0.85336879
0.84920213 0.85345745 0.84920213 0.85345745 0.84911348 0.85771277
0.84494681 0.85345745 0.84494681 0.85345745 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83687943 0.84113475
nan nan 0.8410461 0.84937943 0.85780142 0.8535461
0.84920213 0.85345745 0.84920213 0.85345745 0.84911348 0.85771277
0.84920213 0.85345745 0.84494681 0.85345745 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.84113475 0.84113475 0.84530142 0.84937943
0.84920213 0.85345745 0.84920213 0.85345745 0.84911348 0.85771277
0.84920213 0.85345745 0.84920213 0.85345745 nan nan
nan nan nan nan nan nan
nan nan 0.83696809 0.83696809 0.83696809 0.83696809
nan nan 0.83696809 0.83696809 0.84113475 0.84113475]
category=UserWarning
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters
"Setting penalty='none' will ignore the C and l1_ratio "
x:\programas\python\python37\lib\site-packages\sklearn\linear_model\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
GridSearchCV(cv=5,
estimator=Pipeline(steps=[('preprocessing',
ColumnTransformer(remainder='passthrough',
transformers=[('drop',
'drop',
['Unnamed: '
'0',
'kfold']),
('one_hot',
OneHotEncoder(handle_unknown='ignore'),
['cp',
'restecg',
'slope',
'ca',
'thal']),
('normalize',
MinMaxScaler(),
['age',
'trestbps',
'chol',
'thalach',
'oldpeak'])])),
('algoritmo',
LogisticRegression(solver='liblinear'))]),
param_grid={'algoritmo__C': [1e-05, 0.0001, 0.001, 0.01, 0.1, 1],
'algoritmo__max_iter': [10, 20, 50, 100],
'algoritmo__penalty': ['l2', 'elasticnet', 'none'],
'algoritmo__solver': ['newton-cg', 'lbfgs',
'liblinear', 'sag', 'saga'],
'preprocessing__normalize': [MinMaxScaler(),
StandardScaler()]},
scoring='accuracy', verbose=3)
print(grid_search_logr.best_params_)
{'algoritmo__C': 0.1, 'algoritmo__max_iter': 10, 'algoritmo__penalty': 'none', 'algoritmo__solver': 'sag', 'preprocessing__normalize': StandardScaler()}
display(pd.DataFrame(grid_search_logr.cv_results_))
| mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_algoritmo__C | param_algoritmo__max_iter | param_algoritmo__penalty | param_algoritmo__solver | param_preprocessing__normalize | params | split0_test_score | split1_test_score | split2_test_score | split3_test_score | split4_test_score | mean_test_score | std_test_score | rank_test_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.009948 | 0.000635 | 0.004598 | 0.000484 | 0.00001 | 10 | l2 | newton-cg | MinMaxScaler() | {'algoritmo__C': 1e-05, 'algoritmo__max_iter':... | 0.541667 | 0.541667 | 0.541667 | 0.541667 | 0.553191 | 0.543972 | 0.004610 | 353 |
| 1 | 0.010191 | 0.000691 | 0.004024 | 0.000005 | 0.00001 | 10 | l2 | newton-cg | StandardScaler() | {'algoritmo__C': 1e-05, 'algoritmo__max_iter':... | 0.541667 | 0.541667 | 0.541667 | 0.541667 | 0.553191 | 0.543972 | 0.004610 | 353 |
| 2 | 0.009562 | 0.001203 | 0.004498 | 0.000447 | 0.00001 | 10 | l2 | lbfgs | MinMaxScaler() | {'algoritmo__C': 1e-05, 'algoritmo__max_iter':... | 0.541667 | 0.541667 | 0.541667 | 0.541667 | 0.553191 | 0.543972 | 0.004610 | 353 |
| 3 | 0.009366 | 0.000496 | 0.004091 | 0.000488 | 0.00001 | 10 | l2 | lbfgs | StandardScaler() | {'algoritmo__C': 1e-05, 'algoritmo__max_iter':... | 0.541667 | 0.541667 | 0.541667 | 0.541667 | 0.553191 | 0.543972 | 0.004610 | 353 |
| 4 | 0.010159 | 0.004111 | 0.004993 | 0.001256 | 0.00001 | 10 | l2 | liblinear | MinMaxScaler() | {'algoritmo__C': 1e-05, 'algoritmo__max_iter':... | 0.833333 | 0.875000 | 0.895833 | 0.791667 | 0.765957 | 0.832358 | 0.048812 | 301 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 715 | 0.006996 | 0.000633 | 0.000000 | 0.000000 | 1 | 100 | none | liblinear | StandardScaler() | {'algoritmo__C': 1, 'algoritmo__max_iter': 100... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 513 |
| 716 | 0.011951 | 0.000883 | 0.004189 | 0.000399 | 1 | 100 | none | sag | MinMaxScaler() | {'algoritmo__C': 1, 'algoritmo__max_iter': 100... | 0.833333 | 0.812500 | 0.895833 | 0.770833 | 0.872340 | 0.836968 | 0.044050 | 195 |
| 717 | 0.013444 | 0.001757 | 0.004404 | 0.000815 | 1 | 100 | none | sag | StandardScaler() | {'algoritmo__C': 1, 'algoritmo__max_iter': 100... | 0.833333 | 0.812500 | 0.895833 | 0.770833 | 0.872340 | 0.836968 | 0.044050 | 195 |
| 718 | 0.012554 | 0.000476 | 0.004190 | 0.000386 | 1 | 100 | none | saga | MinMaxScaler() | {'algoritmo__C': 1, 'algoritmo__max_iter': 100... | 0.833333 | 0.812500 | 0.895833 | 0.791667 | 0.872340 | 0.841135 | 0.038193 | 153 |
| 719 | 0.013367 | 0.000478 | 0.004378 | 0.000475 | 1 | 100 | none | saga | StandardScaler() | {'algoritmo__C': 1, 'algoritmo__max_iter': 100... | 0.833333 | 0.812500 | 0.895833 | 0.791667 | 0.872340 | 0.841135 | 0.038193 | 153 |
720 rows × 18 columns
model = Pipeline([
('preprocessing', ct),
('algoritmo',SVC(tol=1e-4))
])
param_grid_SVM = dict(algoritmo__kernel=['linear','poly','rbf','sigmoid','precomputed'],
algoritmo__C=[1e-5, 1e-4, 1e-3, 1e-2, 0.1, 0.5, 1],
algoritmo__gamma=['scale','auto'],
algoritmo__max_iter=[10,20,50,100],
preprocessing__normalize=[MinMaxScaler(), StandardScaler()])
grid_search_SVM = GridSearchCV(model,param_grid=param_grid_SVM,cv=5,verbose=3,scoring='accuracy')
grid_search_SVM.fit(training_X,training_Y.ravel())
Fitting 5 folds for each of 560 candidates, totalling 2800 fits [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.604 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
[CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1e-05, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.604 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.0001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.604 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.001, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.745 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.574 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.604 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.604 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.604 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
[CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.01, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.660 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.958 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.521 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.625 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.574 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.604 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.660 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.958 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.468 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.511 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.660 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.396 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.596 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.660 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.521 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.604 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.354 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.521 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.894 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.625 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.396 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.596 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.660 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.604 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.638 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=0.5, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.521 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.340 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.417 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.500 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.489 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.479 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.596 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.660 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.583 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.771 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=scale, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.521 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.340 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.617 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=linear, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.745 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.583 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.574 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.479 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.438 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.458 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.447 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.562 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.596 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.708 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=poly, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.702 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.708 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.562 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.553 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.723 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=rbf, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=0.702 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=10). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.542 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.646 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.688 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=0.681 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.750 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.625 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=20). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning) x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler. % self.max_iter, ConvergenceWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=50). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=sigmoid, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=0.851 total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=10, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
[CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=20, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=50, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=MinMaxScaler();, score=nan total time= 0.0s [CV 1/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 2/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 3/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 4/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s [CV 5/5] END algoritmo__C=1, algoritmo__gamma=auto, algoritmo__kernel=precomputed, algoritmo__max_iter=100, preprocessing__normalize=StandardScaler();, score=nan total time= 0.0s
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 191x25 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "x:\programas\python\python37\lib\site-packages\sklearn\pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py", line 190, in fit
.format(X.shape[0], X.shape[1]))
ValueError: Precomputed matrix must be a square matrix. Input is a 192x24 matrix.
FitFailedWarning)
x:\programas\python\python37\lib\site-packages\sklearn\model_selection\_search.py:925: UserWarning: One or more of the test scores are non-finite: [0.73625887 0.74423759 0.80292553 0.69450355 0.78652482 0.77819149
0.54397163 0.54397163 0.7072695 0.46028369 0.69397163 0.46462766
0.83679078 0.69042553 0.54397163 0.54397163 0.79512411 0.74875887
0.72420213 0.69051418 0.81179078 0.77402482 0.54397163 0.54397163
0.73218085 0.72801418 0.74051418 0.68218085 0.77819149 0.75328014
0.54397163 0.54397163 nan nan nan nan
nan nan nan nan 0.73625887 0.74423759
0.80292553 0.69450355 0.78652482 0.77819149 0.54397163 0.54397163
0.7072695 0.46028369 0.69397163 0.46462766 0.83679078 0.69042553
0.54397163 0.54397163 0.76569149 0.74884752 0.76117021 0.71985816
0.78652482 0.76994681 0.54397163 0.54397163 0.7445922 0.77375887
0.74450355 0.74069149 0.78661348 0.74494681 0.54397163 0.54397163
nan nan nan nan nan nan
nan nan 0.73625887 0.74423759 0.80292553 0.69450355
0.78652482 0.76569149 0.54397163 0.54397163 0.7072695 0.46028369
0.69397163 0.46462766 0.83679078 0.69042553 0.54397163 0.54397163
0.79512411 0.74875887 0.72420213 0.69051418 0.80762411 0.77402482
0.54397163 0.54397163 0.73218085 0.72801418 0.74051418 0.68218085
0.77819149 0.75328014 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
0.73625887 0.74423759 0.80292553 0.69450355 0.78652482 0.76569149
0.54397163 0.54397163 0.7072695 0.46028369 0.69397163 0.46462766
0.83679078 0.69042553 0.54397163 0.54397163 0.76569149 0.74884752
0.76117021 0.71985816 0.78652482 0.76994681 0.54397163 0.54397163
0.7445922 0.77375887 0.74450355 0.74069149 0.78661348 0.74494681
0.54397163 0.54397163 nan nan nan nan
nan nan nan nan 0.72792553 0.74423759
0.77792553 0.68617021 0.79494681 0.78244681 0.54397163 0.54397163
0.7072695 0.46028369 0.69397163 0.46462766 0.81994681 0.69042553
0.54397163 0.54397163 0.79512411 0.74875887 0.72420213 0.69051418
0.80762411 0.77402482 0.54397163 0.54397163 0.73218085 0.72801418
0.74051418 0.68218085 0.77819149 0.75328014 0.54397163 0.54397163
nan nan nan nan nan nan
nan nan 0.72792553 0.74423759 0.77792553 0.68617021
0.79494681 0.78244681 0.54397163 0.54397163 0.7072695 0.46028369
0.69397163 0.46462766 0.83679078 0.69042553 0.54397163 0.54397163
0.76569149 0.74884752 0.76117021 0.71985816 0.79069149 0.76994681
0.54397163 0.54397163 0.7445922 0.77375887 0.74450355 0.74069149
0.78661348 0.74494681 0.54397163 0.54397163 nan nan
nan nan nan nan nan nan
0.7445922 0.74840426 0.79042553 0.74069149 0.81578014 0.78235816
0.85345745 0.79893617 0.73643617 0.46028369 0.71072695 0.46462766
0.82420213 0.6945922 0.71968085 0.54397163 0.79512411 0.74875887
0.74086879 0.69051418 0.82429078 0.78244681 0.54397163 0.54397163
0.73218085 0.72801418 0.7447695 0.71560284 0.77828014 0.7320922
0.54397163 0.54397163 nan nan nan nan
nan nan nan nan 0.7445922 0.74840426
0.79042553 0.74069149 0.81578014 0.78235816 0.85345745 0.79893617
0.7072695 0.46028369 0.69397163 0.46462766 0.83679078 0.69042553
0.54397163 0.54397163 0.76569149 0.74884752 0.76117021 0.71985816
0.79069149 0.75744681 0.54397163 0.54397163 0.7445922 0.77375887
0.74033688 0.74485816 0.78244681 0.74911348 0.54397163 0.54397163
nan nan nan nan nan nan
nan nan 0.76108156 0.72402482 0.76542553 0.81551418
0.85345745 0.85762411 0.84929078 0.84494681 0.58528369 0.46019504
0.68572695 0.45602837 0.85753546 0.58102837 0.86586879 0.75319149
0.77012411 0.75292553 0.78679078 0.73218085 0.83679078 0.79902482
0.84078014 0.78235816 0.73218085 0.66551418 0.73218085 0.71578014
0.79902482 0.78679078 0.82810284 0.80328014 nan nan
nan nan nan nan nan nan
0.76108156 0.72402482 0.76542553 0.81551418 0.85345745 0.85762411
0.84929078 0.84494681 0.7072695 0.46028369 0.69397163 0.46462766
0.82845745 0.67792553 0.54397163 0.54397163 0.76569149 0.74884752
0.78200355 0.76569149 0.81152482 0.76994681 0.83262411 0.79485816
0.73625887 0.77375887 0.74450355 0.75336879 0.78244681 0.78687943
0.64441489 0.79485816 nan nan nan nan
nan nan nan nan 0.60664894 0.64024823
0.79060284 0.79095745 0.84929078 0.81994681 0.84929078 0.83253546
0.58617021 0.50602837 0.77828014 0.47269504 0.83661348 0.76994681
0.84911348 0.7947695 0.72012411 0.75292553 0.82039007 0.79884752
0.86586879 0.81985816 0.86161348 0.82420213 0.73218085 0.69033688
0.71134752 0.80328014 0.78634752 0.82420213 0.79060284 0.85771277
nan nan nan nan nan nan
nan nan 0.60664894 0.64024823 0.79060284 0.79095745
0.84929078 0.81994681 0.84929078 0.83253546 0.7447695 0.46019504
0.74911348 0.45602837 0.83687943 0.52269504 0.54397163 0.60265957
0.76152482 0.75718085 0.76533688 0.71161348 0.85771277 0.83687943
0.84503546 0.82819149 0.7320922 0.7695922 0.74450355 0.72411348
0.83670213 0.80336879 0.83670213 0.82819149 nan nan
nan nan nan nan nan nan
0.58173759 0.62641844 0.69450355 0.70257092 0.80718085 0.77810284
0.84902482 0.83670213 0.68262411 0.44769504 0.80762411 0.48120567
0.86187943 0.84086879 0.84929078 0.81551418 0.6731383 0.73164894
0.79078014 0.74858156 0.85319149 0.82836879 0.85319149 0.84086879
0.73634752 0.6856383 0.71542553 0.7072695 0.7572695 0.85345745
0.78218085 0.84503546 nan nan nan nan
nan nan nan nan 0.58173759 0.62641844
0.69450355 0.70257092 0.80718085 0.77810284 0.84902482 0.83670213
0.7447695 0.46019504 0.71489362 0.45602837 0.82836879 0.52269504
0.55664894 0.71542553 0.76985816 0.6731383 0.79884752 0.7785461
0.85771277 0.84929078 0.85345745 0.83679078 0.7320922 0.68661348
0.72367021 0.79095745 0.83670213 0.8535461 0.8535461 0.8535461
nan nan nan nan nan nan
nan nan]
category=UserWarning
x:\programas\python\python37\lib\site-packages\sklearn\svm\_base.py:258: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
GridSearchCV(cv=5,
estimator=Pipeline(steps=[('preprocessing',
ColumnTransformer(remainder='passthrough',
transformers=[('drop',
'drop',
['Unnamed: '
'0',
'kfold']),
('one_hot',
OneHotEncoder(handle_unknown='ignore'),
['cp',
'restecg',
'slope',
'ca',
'thal']),
('normalize',
MinMaxScaler(),
['age',
'trestbps',
'chol',
'thalach',
'oldpeak'])])),
('algoritmo', SVC(tol=0.0001))]),
param_grid={'algoritmo__C': [1e-05, 0.0001, 0.001, 0.01, 0.1, 0.5,
1],
'algoritmo__gamma': ['scale', 'auto'],
'algoritmo__kernel': ['linear', 'poly', 'rbf',
'sigmoid', 'precomputed'],
'algoritmo__max_iter': [10, 20, 50, 100],
'preprocessing__normalize': [MinMaxScaler(),
StandardScaler()]},
scoring='accuracy', verbose=3)
print(grid_search_SVM.best_params_)
{'algoritmo__C': 0.1, 'algoritmo__gamma': 'scale', 'algoritmo__kernel': 'poly', 'algoritmo__max_iter': 100, 'preprocessing__normalize': MinMaxScaler()}
display(pd.DataFrame(grid_search_SVM.cv_results_))
| mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_algoritmo__C | param_algoritmo__gamma | param_algoritmo__kernel | param_algoritmo__max_iter | param_preprocessing__normalize | params | split0_test_score | split1_test_score | split2_test_score | split3_test_score | split4_test_score | mean_test_score | std_test_score | rank_test_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.010565 | 0.001741 | 0.005591 | 0.001188 | 0.00001 | scale | linear | 10 | MinMaxScaler() | {'algoritmo__C': 1e-05, 'algoritmo__gamma': 's... | 0.687500 | 0.791667 | 0.770833 | 0.729167 | 0.702128 | 0.736259 | 0.039644 | 248 |
| 1 | 0.007578 | 0.000488 | 0.003988 | 0.000004 | 0.00001 | scale | linear | 10 | StandardScaler() | {'algoritmo__C': 1e-05, 'algoritmo__gamma': 's... | 0.729167 | 0.770833 | 0.916667 | 0.687500 | 0.617021 | 0.744238 | 0.100069 | 230 |
| 2 | 0.007379 | 0.000488 | 0.004388 | 0.000489 | 0.00001 | scale | linear | 20 | MinMaxScaler() | {'algoritmo__C': 1e-05, 'algoritmo__gamma': 's... | 0.854167 | 0.812500 | 0.812500 | 0.833333 | 0.702128 | 0.802926 | 0.052714 | 89 |
| 3 | 0.008179 | 0.001164 | 0.004208 | 0.000438 | 0.00001 | scale | linear | 20 | StandardScaler() | {'algoritmo__C': 1e-05, 'algoritmo__gamma': 's... | 0.708333 | 0.750000 | 0.854167 | 0.479167 | 0.680851 | 0.694504 | 0.122735 | 306 |
| 4 | 0.007888 | 0.000180 | 0.004181 | 0.000384 | 0.00001 | scale | linear | 50 | MinMaxScaler() | {'algoritmo__C': 1e-05, 'algoritmo__gamma': 's... | 0.812500 | 0.812500 | 0.770833 | 0.770833 | 0.765957 | 0.786525 | 0.021283 | 125 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 555 | 0.007572 | 0.000492 | 0.000000 | 0.000000 | 1 | auto | precomputed | 20 | StandardScaler() | {'algoritmo__C': 1, 'algoritmo__gamma': 'auto'... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 462 |
| 556 | 0.006776 | 0.000745 | 0.000000 | 0.000000 | 1 | auto | precomputed | 50 | MinMaxScaler() | {'algoritmo__C': 1, 'algoritmo__gamma': 'auto'... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 463 |
| 557 | 0.007580 | 0.000814 | 0.000000 | 0.000000 | 1 | auto | precomputed | 50 | StandardScaler() | {'algoritmo__C': 1, 'algoritmo__gamma': 'auto'... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 465 |
| 558 | 0.007190 | 0.000383 | 0.000000 | 0.000000 | 1 | auto | precomputed | 100 | MinMaxScaler() | {'algoritmo__C': 1, 'algoritmo__gamma': 'auto'... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 550 |
| 559 | 0.007382 | 0.000479 | 0.000000 | 0.000000 | 1 | auto | precomputed | 100 | StandardScaler() | {'algoritmo__C': 1, 'algoritmo__gamma': 'auto'... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 560 |
560 rows × 18 columns
model = Pipeline([
('preprocessing', ct),
('algoritmo', KNeighborsClassifier())
])
param_grid_KNN = dict(algoritmo__n_neighbors=[3,5,9,15,25],
algoritmo__weights=['uniform','distance'],
algoritmo__algorithm=['auto','ball_tree','kd_tree','brute'],
preprocessing__normalize=[MinMaxScaler(), StandardScaler()])
grid_search_KNN = GridSearchCV(model,param_grid=param_grid_KNN,cv=5,verbose=3,scoring='accuracy')
grid_search_KNN.fit(training_X,training_Y.ravel())
Fitting 5 folds for each of 80 candidates, totalling 400 fits [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=auto, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=ball_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=kd_tree, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.766 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.667 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=3, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.812 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=5, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.830 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=9, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.792 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.729 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=15, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.854 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.896 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=uniform, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.875 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.917 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.812 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=MinMaxScaler();, score=0.809 total time= 0.0s [CV 1/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.833 total time= 0.0s [CV 2/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.792 total time= 0.0s [CV 3/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.938 total time= 0.0s [CV 4/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.771 total time= 0.0s [CV 5/5] END algoritmo__algorithm=brute, algoritmo__n_neighbors=25, algoritmo__weights=distance, preprocessing__normalize=StandardScaler();, score=0.787 total time= 0.0s
GridSearchCV(cv=5,
estimator=Pipeline(steps=[('preprocessing',
ColumnTransformer(remainder='passthrough',
transformers=[('drop',
'drop',
['Unnamed: '
'0',
'kfold']),
('one_hot',
OneHotEncoder(handle_unknown='ignore'),
['cp',
'restecg',
'slope',
'ca',
'thal']),
('normalize',
MinMaxScaler(),
['age',
'trestbps',
'chol',
'thalach',
'oldpeak'])])),
('algoritmo', KNeighborsClassifier())]),
param_grid={'algoritmo__algorithm': ['auto', 'ball_tree',
'kd_tree', 'brute'],
'algoritmo__n_neighbors': [3, 5, 9, 15, 25],
'algoritmo__weights': ['uniform', 'distance'],
'preprocessing__normalize': [MinMaxScaler(),
StandardScaler()]},
scoring='accuracy', verbose=3)
print(grid_search_KNN.best_params_)
{'algoritmo__algorithm': 'auto', 'algoritmo__n_neighbors': 25, 'algoritmo__weights': 'distance', 'preprocessing__normalize': MinMaxScaler()}
knn_resultados = pd.DataFrame(grid_search_KNN.cv_results_)
display(knn_resultados)
| mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_algoritmo__algorithm | param_algoritmo__n_neighbors | param_algoritmo__weights | param_preprocessing__normalize | params | split0_test_score | split1_test_score | split2_test_score | split3_test_score | split4_test_score | mean_test_score | std_test_score | rank_test_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.007959 | 0.000911 | 0.005602 | 0.000499 | auto | 3 | uniform | MinMaxScaler() | {'algoritmo__algorithm': 'auto', 'algoritmo__n... | 0.812500 | 0.833333 | 0.895833 | 0.791667 | 0.787234 | 0.824113 | 0.039437 | 49 |
| 1 | 0.006892 | 0.000179 | 0.005585 | 0.000489 | auto | 3 | uniform | StandardScaler() | {'algoritmo__algorithm': 'auto', 'algoritmo__n... | 0.812500 | 0.729167 | 0.916667 | 0.666667 | 0.787234 | 0.782447 | 0.083887 | 73 |
| 2 | 0.006782 | 0.000747 | 0.004388 | 0.000488 | auto | 3 | distance | MinMaxScaler() | {'algoritmo__algorithm': 'auto', 'algoritmo__n... | 0.812500 | 0.791667 | 0.875000 | 0.791667 | 0.765957 | 0.807358 | 0.036901 | 69 |
| 3 | 0.007180 | 0.000746 | 0.004389 | 0.000489 | auto | 3 | distance | StandardScaler() | {'algoritmo__algorithm': 'auto', 'algoritmo__n... | 0.812500 | 0.729167 | 0.916667 | 0.666667 | 0.787234 | 0.782447 | 0.083887 | 73 |
| 4 | 0.006582 | 0.000489 | 0.005784 | 0.000399 | auto | 5 | uniform | MinMaxScaler() | {'algoritmo__algorithm': 'auto', 'algoritmo__n... | 0.854167 | 0.833333 | 0.895833 | 0.729167 | 0.808511 | 0.824202 | 0.055480 | 45 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 75 | 0.007181 | 0.000399 | 0.004622 | 0.000858 | brute | 15 | distance | StandardScaler() | {'algoritmo__algorithm': 'brute', 'algoritmo__... | 0.854167 | 0.833333 | 0.937500 | 0.729167 | 0.787234 | 0.828280 | 0.069474 | 41 |
| 76 | 0.007380 | 0.000489 | 0.005186 | 0.000399 | brute | 25 | uniform | MinMaxScaler() | {'algoritmo__algorithm': 'brute', 'algoritmo__... | 0.854167 | 0.895833 | 0.937500 | 0.812500 | 0.808511 | 0.861702 | 0.049431 | 5 |
| 77 | 0.007171 | 0.000404 | 0.005585 | 0.000489 | brute | 25 | uniform | StandardScaler() | {'algoritmo__algorithm': 'brute', 'algoritmo__... | 0.833333 | 0.791667 | 0.937500 | 0.770833 | 0.787234 | 0.824113 | 0.060321 | 49 |
| 78 | 0.007181 | 0.000399 | 0.004587 | 0.000489 | brute | 25 | distance | MinMaxScaler() | {'algoritmo__algorithm': 'brute', 'algoritmo__... | 0.875000 | 0.916667 | 0.937500 | 0.812500 | 0.808511 | 0.870035 | 0.052624 | 1 |
| 79 | 0.006981 | 0.000631 | 0.004787 | 0.000399 | brute | 25 | distance | StandardScaler() | {'algoritmo__algorithm': 'brute', 'algoritmo__... | 0.833333 | 0.791667 | 0.937500 | 0.770833 | 0.787234 | 0.824113 | 0.060321 | 49 |
80 rows × 17 columns
best_logr = grid_search_logr.best_estimator_
pred_logr = best_logr.predict(training_X)
print('----------Reporte para el mejor modelo de regresión logística----------')
print(classification_report(training_Y,pred_logr))
best_SVM = grid_search_SVM.best_estimator_
pred_SVM = best_SVM.predict(training_X)
print('----------Reporte para el mejor modelo de SVM----------')
print(classification_report(training_Y,pred_SVM))
best_KNN = grid_search_KNN.best_estimator_
pred_KNN = best_KNN.predict(training_X)
print('----------Reporte para los mejores modelos de KNN----------')
print(knn_resultados['mean_test_score'][knn_resultados['rank_test_score']==1])
----------Reporte para el mejor modelo de regresión logística----------
precision recall f1-score support
0 0.88 0.92 0.90 130
1 0.90 0.85 0.88 109
accuracy 0.89 239
macro avg 0.89 0.89 0.89 239
weighted avg 0.89 0.89 0.89 239
----------Reporte para el mejor modelo de SVM----------
precision recall f1-score support
0 0.88 0.95 0.92 130
1 0.94 0.84 0.89 109
accuracy 0.90 239
macro avg 0.91 0.90 0.90 239
weighted avg 0.91 0.90 0.90 239
----------Reporte para los mejores modelos de KNN----------
18 0.870035
38 0.870035
58 0.870035
78 0.870035
Name: mean_test_score, dtype: float64
test_X = data_test.drop(['label'],axis=1)
test_Y = data_test['label']
pred_test_Y=best_SVM.predict(test_X)
print('----------Reporte para el mejor modelo con datos nuevos----------')
print(classification_report(test_Y, pred_test_Y))
----------Reporte para el mejor modelo con datos nuevos----------
precision recall f1-score support
0 0.78 0.93 0.85 30
1 0.91 0.71 0.80 28
accuracy 0.83 58
macro avg 0.84 0.82 0.82 58
weighted avg 0.84 0.83 0.83 58
dump(best_SVM, 'best_model.joblib')
best_model = load('best_model.joblib')